The second study, presented in Table REF , focuses on the internal behavior of the SegNet model's latent space when adding contour-based auxiliary tasks.
These tasks are edge detection (E) [1]}, semantic contours (C) [2]}, quantized distance transform (D) [3]}, and semantic segmentation (S) [4]} as the main task.
Here, we use the segmentation metrics to evaluate the predicted regions.
To evaluate the behavior (distribution), we use several clustering metrics.
We notice a direct correlation between clustering behavior and segmentation results.
These quantitative results on CamVid are complementary results to those presented in Section REF .
In Table REF , our best results are produced by using both loss (cross-entropy and loss-IoU), data augmentation, and all tasks.
We replicate this setting in Cityscapes and Freiburg Forest datasets.
